The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Emilie Murphy - One of the best experts on this subject based on the ideXlab platform.
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remote sensing based Yield Monitoring application to winter wheat in united states and ukraine
International Journal of Applied Earth Observation and Geoinformation, 2019Co-Authors: Belen Franch, E Vermote, Sergii Skakun, Jeanclaude Roger, Inbal Beckerreshef, Emilie MurphyAbstract:Abstract Accurate and timely crop Yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural Monitoring, including crop Yield assessment and forecasting. In this study, we present a new crop Yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat Yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat Yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).
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a 30 year avhrr land surface reflectance climate data record and its application to wheat Yield Monitoring
Remote Sensing, 2017Co-Authors: Belen Franch, E Vermote, Jeanclaude Roger, Inbal Beckerreshef, Emilie Murphy, C O Justice, Martin Claverie, Jyoteshwar Nagol, Ivan Csiszar, Dave MeyerAbstract:The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration Monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat Yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.
George Vellidis - One of the best experts on this subject based on the ideXlab platform.
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An Optical Yield Monitor for Peanuts – Proof of Concept and Evaluation
Advances in Animal Biosciences, 2017Co-Authors: E. Porter, George Vellidis, V. Liakos, W. Porter, B. BranchAbstract:Peanut (Arachis Hypogea) is one of the few major agronomic crops for which a Yield monitor is not commercially available. This paper describes an ongoing project whose long-term goal is to adapt an optical sensor originally developed for cotton Yield Monitoring for use as a peanut Yield monitor (PYM). The immediate objective of the work reported here was to evaluate the PYM under harvest conditions typical in southern Georgia, USA. The PYM consists of two mass-flow sensors, a data acquisition system, and a DGPS receiver. The PYM was evaluated on three fields totaling 29 ha during the 2016 harvest season. Percent error between the scale load and calculated load was 2% or better for the first field tested, but increased greatly for subsequent fields that were tested, most likely caused by damage to the sensor lens from the impact of pebbles.
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Evaluating Performance of New Generation Cotton Yield Monitors
2008 Providence Rhode Island June 29 - July 2 2008, 2008Co-Authors: Calvin D. Perry, George VellidisAbstract:The performance of the new generation Ag Leader cotton Yield Monitoring system, based on the Insight display, was evaluated for Yield accuracy and ease of use during the 2007 harvest season. A four-sensor Ag Leader Insight system was installed on a grower-owned John Deere 9996 cotton picker in south Georgia and was used to harvest his 25 fields. Basket load, module, and field total weights were compared to Yield monitor weights to evaluate accuracy. The grower's experience with the system was used to evaluate ease of use. After proper initial weight calibration, the system under-predicted actual weight with a percent error of approximately 12% when comparing basket loads, modules or field totals. Nevertheless, the predicted weight values were fairly consistent and the resulting Yield data produced Yield maps that represented field variations quite well. Once re-calibrated later in the season, the system began over-predicting cotton weight and the percent error reduced to approximately 7%. Ease of use was very good with the grower able to navigate menus and settings easily.
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SIMULTANEOUS ASSESSMENT OF COTTON Yield MONITOR
Applied Engineering in Agriculture, 2003Co-Authors: George Vellidis, Calvin D. Perry, D. L. Thomas, Glen C. Rains, N. Wells, C. K. KvienAbstract:The most essential component of precision farming is the Yield monitor –– a sensor or group of sensors installed on harvesting equipment that dynamically measures spatial Yield variability. Yield maps, which are produced with data from Yield monitors, are extremely useful in providing a visual image to clearly show the variability of Yield across a field. In response to the demand for a reliable and accurate cotton Yield monitor, several have recently become commercially available. We assessed the AgLeader, Agri–Plan, FarmScan, and Micro–Trak cotton Yield monitors in southern Georgia for five harvest seasons from 1997 to 2001. During 2001 we also assessed a prototype Yield monitor. Each year, two or more Yield monitors were mounted on a cotton harvester and were used during the harvest of several farmer–owned and managed fields. The accuracy of each Yield monitor was tested by comparing the weight of each harvested load to data produced by the Yield monitor. Yield maps from each Yield monitor were also produced with the respective software packages and compared. Features of the monitors were also compared. Each of the cotton Yield Monitoring systems we assessed has something to offer a user interested in creating Yield maps. All are capable of producing an adequate Yield map provided the system is properly calibrated, operated, and maintained.
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Simultaneous Assessment of Cotton Yield Monitors
2002 Chicago IL July 28-31 2002, 2002Co-Authors: George Vellidis, Calvin D. Perry, D. L. Thomas, Rodney Hill, Glen C. Rains, Dewayne DalesAbstract:The most essential component of precision farming is the Yield monitor . a sensor . or group of sensors . installed on harvesting equipment that dynamically measure spatial Yield variability. Yield maps, which are produced using data from Yield monitors, are extremely useful in providing a visual image to clearly show the variability of Yield across a field. In response to the demand for a reliable and accurate cotton Yield monitor, several monitors have recently become commercially available. We assessed the AgLeader, AgriPlan, FarmScan, and Micro-Trak cotton Yield monitors in southern Georgia for five harvest seasons between 1997 and 2001. During 2001 we also assessed a prototype Yield monitor. Each year, three or four Yield monitors were mounted on a cotton harvester and used during harvest of several farmer-owned and managed fields. The accuracy of each sensor was tested by comparing the weight of each harvested load to data produced by the Yield monitors. Yield maps from each Yield monitor were also produced with the respective software packages and compared. Feature comparisons of each monitor were included. Each of the cotton Yield Monitoring systems we assessed have something to offer a user interested in creating Yield maps. All are capable of producing an adequate Yield map provided the system is properly calibrated, operated, and maintained.
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THE PEANUT Yield Monitoring SYSTEM
Transactions of the ASAE, 2001Co-Authors: George Vellidis, J. S. Durrence, T. K. Hamrita, Calvin D. Perry, D. L. Thomas, Rodney Hill, C. K. Kvien, Glen C. RainsAbstract:The most essential component of precision farming is the Yield monitor, a sensor or group of sensors installed on harvesting equipment that dynamically measure spatial Yield variability. Yield maps, which are produced using data from Yield monitors, are extremely useful in providing the farmer a color–coded visual image clearly showing the variability of Yield across a field. University of Georgia scientists recently completed development work on PYMS, the Peanut Yield Monitoring System. PYMS uses load cells for instantaneous load measurements of harvested peanuts and has proven to be accurate to between 2% and 3% on a trailer–load basis and to approximately 1% on a field basis when using data collected during combine operation. PYMS data are accurate to around 1% on a basket–load basis when using data collected under static conditions. The instantaneous accuracy of PYMS was calculated to be 700 kg/ha. Basing management decisions on the Yield of individual pixels of PYMS Yield maps is not realistic. The strength of PYMS is in differentiating Yield trends and evaluating management practices. The system was extensively and successfully field–tested over a 3–year period and evaluated by 11 users during 1999, all of whom were able to use the resulting Yield maps to evaluate current management practices or to develop future management plans. The University of Georgia has submitted a patent application for PYMS, and the technology has been licensed.
Belen Franch - One of the best experts on this subject based on the ideXlab platform.
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remote sensing based Yield Monitoring application to winter wheat in united states and ukraine
International Journal of Applied Earth Observation and Geoinformation, 2019Co-Authors: Belen Franch, E Vermote, Sergii Skakun, Jeanclaude Roger, Inbal Beckerreshef, Emilie MurphyAbstract:Abstract Accurate and timely crop Yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural Monitoring, including crop Yield assessment and forecasting. In this study, we present a new crop Yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat Yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat Yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).
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a 30 year avhrr land surface reflectance climate data record and its application to wheat Yield Monitoring
Remote Sensing, 2017Co-Authors: Belen Franch, E Vermote, Jeanclaude Roger, Inbal Beckerreshef, Emilie Murphy, C O Justice, Martin Claverie, Jyoteshwar Nagol, Ivan Csiszar, Dave MeyerAbstract:The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration Monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat Yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.
J. S. Durrence - One of the best experts on this subject based on the ideXlab platform.
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THE PEANUT Yield Monitoring SYSTEM
Transactions of the ASAE, 2001Co-Authors: George Vellidis, J. S. Durrence, T. K. Hamrita, Calvin D. Perry, D. L. Thomas, Rodney Hill, C. K. Kvien, Glen C. RainsAbstract:The most essential component of precision farming is the Yield monitor, a sensor or group of sensors installed on harvesting equipment that dynamically measure spatial Yield variability. Yield maps, which are produced using data from Yield monitors, are extremely useful in providing the farmer a color–coded visual image clearly showing the variability of Yield across a field. University of Georgia scientists recently completed development work on PYMS, the Peanut Yield Monitoring System. PYMS uses load cells for instantaneous load measurements of harvested peanuts and has proven to be accurate to between 2% and 3% on a trailer–load basis and to approximately 1% on a field basis when using data collected during combine operation. PYMS data are accurate to around 1% on a basket–load basis when using data collected under static conditions. The instantaneous accuracy of PYMS was calculated to be 700 kg/ha. Basing management decisions on the Yield of individual pixels of PYMS Yield maps is not realistic. The strength of PYMS is in differentiating Yield trends and evaluating management practices. The system was extensively and successfully field–tested over a 3–year period and evaluated by 11 users during 1999, all of whom were able to use the resulting Yield maps to evaluate current management practices or to develop future management plans. The University of Georgia has submitted a patent application for PYMS, and the technology has been licensed.
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A Load Cell Based Yield Monitor for Peanut Feasibility Study
Precision Agriculture, 1999Co-Authors: J. S. Durrence, T. K. Hamrita, George VellidisAbstract:A prototype peanut Yield Monitoring system based on load cell transducers was evaluated for use in precision farming applications. Noise characteristics under simulated field conditions were examined, and the effect of mixing within the peanut combine during harvest was also investigated. Evaluation results showed that the system has potential for providing limited quality site-specific Yield measurements for Yield mapping applications.
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Noise reduction in a load cell based peanut Yield monitor using digital signal processing techniques
Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications o, 1Co-Authors: T. K. Hamrita, J. S. Durrence, George Vellidis, Calvin D. Perry, D. L. Thomas, C. K. KvienAbstract:Precision farming describes the process of measuring and mapping land crop characteristics and then using these measurements to develop precise and intelligent application strategies that improve overall farm production. Yield Monitoring is the phase of precision farming in which the crop Yield variation within a field is measured and mapped. Yield maps from previous seasons can be used to determine the needed inputs in the field, whereas post harvest Yield maps can be used to evaluate the implemented methods and make adjustments for the next season. Grain Yield monitors are available, however there are few if any monitors for other crops. This paper examines the use of strain gauge load cells in a Yield monitor for peanut combines and the development of methods for minimizing measurement noises thereby increasing reliability.
Jeanclaude Roger - One of the best experts on this subject based on the ideXlab platform.
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remote sensing based Yield Monitoring application to winter wheat in united states and ukraine
International Journal of Applied Earth Observation and Geoinformation, 2019Co-Authors: Belen Franch, E Vermote, Sergii Skakun, Jeanclaude Roger, Inbal Beckerreshef, Emilie MurphyAbstract:Abstract Accurate and timely crop Yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural Monitoring, including crop Yield assessment and forecasting. In this study, we present a new crop Yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat Yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat Yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).
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a 30 year avhrr land surface reflectance climate data record and its application to wheat Yield Monitoring
Remote Sensing, 2017Co-Authors: Belen Franch, E Vermote, Jeanclaude Roger, Inbal Beckerreshef, Emilie Murphy, C O Justice, Martin Claverie, Jyoteshwar Nagol, Ivan Csiszar, Dave MeyerAbstract:The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration Monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat Yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.